Papers | Parallel Computing

@article{24:angiography:TMI,

title = {A Convolutional-Transformer Model for FFR and iFR Assessment from Coronary Angiography},

author = {Raffaele Mineo and Federica Salanitri Proietto and Giovanni Bellitto and Isaak Kavasidis and Ovidio. De Filippo and Michele Millesimo and Gaetano Maria De Ferrari and Marco Aldinucci and Daniela Giordano and Simone Palazzo and Fabrizio D'Ascenzo and Concetto Spampinato},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10582501},

doi = {10.1109/TMI.2024.3383283},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Transaction on Medical Imaging},

volume = {43},

number = {8},

pages = {2866-2877},

publisher = {IEEE},

abstract = {The quantification of stenosis severity from X-ray catheter angiography is a challenging task. Indeed, this requires to fully understand the lesion's geometry by analyzing dynamics of the contrast material, only relying on visual observation by clinicians. To support decision making for cardiac intervention, we propose a hybrid CNN-Transformer model for the assessment of angiography-based non-invasive fractional flow-reserve (FFR) and instantaneous wave-free ratio (iFR) of intermediate coronary stenosis. Our approach predicts whether a coronary artery stenosis is hemodynamically significant and provides direct FFR and iFR estimates. This is achieved through a combination of regression and classification branches that forces the model to focus on the cut-off region of FFR (around 0.8 FFR value), which is highly critical for decision-making. We also propose a spatio-temporal factorization mechanisms that redesigns the transformer's self-attention mechanism to capture both local spatial and temporal interactions between vessel geometry, blood flow dynamics, and lesion morphology. The proposed method achieves state-of-the-art performance on a dataset of 778 exams from 389 patients. Unlike existing methods, our approach employs a single angiography view and does not require knowledge of the key frame; supervision at training time is provided by a classification loss (based on a threshold of the FFR/iFR values) and a regression loss for direct estimation. Finally, the analysis of model interpretability and calibration shows that, in spite of the complexity of angiographic imaging data, our method can robustly identify the location of the stenosis and correlate prediction uncertainty to the provided output scores.},

keywords = {ai, cardio},

pubstate = {published},

tppubtype = {article}

}

@article{23:casella:ultra,

title = {Predictors of target lesion failure after treatment of left main, bifurcation, or chronic total occlusion lesions with ultrathin-strut drug-eluting coronary stents in the ULTRA registry},

author = {Ovidio Filippo and Francesco Bruno and Tineke H. Pinxterhuis and Mariusz Gasior and Leor Perl and Luca Gaido and Domenico Tuttolomondo and Antonio Greco and Roberto Verardi and Gianluca Lo Martire and Mario Iannaccone and Attilio Leone and Gaetano Liccardo and Serena Caglioni and Rocio González Ferreiro and Giulio Rodinò and Giuseppe Musumeci and Giuseppe Patti and Irene Borzillo and Giuseppe Tarantini and Wojciech Wańha and Bruno Casella and Eline H Ploumen and Lukasz Pyka and Ran Kornowski and Andrea Gagnor and Raffaele Piccolo and Sergio Raposeiras Roubin and Davide Capodanno and Paolo Zocca and Federico Conrotto and Gaetano M De Ferrari and Clemens Birgelen and Fabrizio D'Ascenzo},

url = {https://onlinelibrary.wiley.com/doi/full/10.1002/ccd.30696},

doi = {10.1002/ccd.30696},

year  = {2023},

date = {2023-01-01},

journal = {Catheterization and Cardiovascular Interventions},

abstract = {Background: Data about the long-term performance of new-generation ultrathin-strut drug-eluting stents (DES) in challenging coronary lesions, such as left main (LM), bifurcation, and chronic total occlusion (CTO) lesions are scant. Methods: The international multicenter retrospective observational ULTRA study included consecutive patients treated from September 2016 to August 2021 with ultrathin-strut (<70µm) DES in challenging de novo lesions. Primary endpoint was target lesion failure (TLF): composite of cardiac death, target-lesion revascularization (TLR), target-vessel myocardial infarction (TVMI), or definite stent thrombosis (ST). Secondary endpoints included all-cause death, acute myocardial infarction (AMI), target vessel revascularization, and TLF components. TLF predictors were assessed with Cox multivariable analysis. Results: Of 1801 patients (age: 66.6$±$11.2 years; male: 1410 [78.3%]), 170 (9.4%) experienced TLF during follow-up of 3.1$±$1.4 years. In patients with LM, CTO, and bifurcation lesions, TLF rates were 13.5%, 9.9%, and 8.9%, respectively. Overall, 160 (8.9%) patients died (74 [4.1%] from cardiac causes). AMI and TVMI rates were 6.0% and 3.2%, respectively. ST occurred in 11 (1.1%) patients while 77 (4.3%) underwent TLR. Multivariable analysis identified the following predictors of TLF: age, STEMI with cardiogenic shock, impaired left ventricular ejection fraction, diabetes, and renal dysfunction. Among the procedural variables, total stent length increased TLF risk (HR: 1.01, 95% CI: 1-1.02 per mm increase), while intracoronary imaging reduced the risk substantially (HR: 0.35, 95% CI: 0.12-0.82). Conclusions: Ultrathin-strut DES showed high efficacy and satisfactory safety, even in patients with challenging coronary lesions. Yet, despite using contemporary gold-standard DES, the association persisted between established patient- and procedure-related features of risk and impaired 3-year clinical outcome.},

keywords = {ai, cardio},

pubstate = {published},

tppubtype = {article}

}

@article{GALLONE202218,

title = {Impact of Left Ventricular Ejection Fraction on Procedural and Long-Term Outcomes of Bifurcation Percutaneous Coronary Intervention},

author = {Guglielmo Gallone and Jeehoon Kang and Francesco Bruno and Jung-Kyu Han and Ovidio De Filippo and Han-Mo Yang and Mattia Doronzo and Kyung-Woo Park and Gianluca Mittone and Hyun-Jae Kang and Radoslaw Parma and Hyeon-Cheol Gwon and Enrico Cerrato and Woo Jung Chun and Grzegorz Smolka and Seung-Ho Hur and Gerard Helft and Seung Hwan Han and Saverio Muscoli and Young Bin Song and Filippo Figini and Ki Hong Choi and Giacomo Boccuzzi and Soon-Jun Hong and Daniela Trabattoni and Chang-Wook Nam and Massimo Giammaria and Hyo-Soo Kim and Federico Conrotto and Javier Escaned and Carlo Di Mario and Fabrizio D'Ascenzo and Bon-Kwon Koo and Gaetano Maria Ferrari},

url = {https://www.sciencedirect.com/science/article/pii/S0002914922001692},

doi = {https://doi.org/10.1016/j.amjcard.2022.02.015},

issn = {0002-9149},

year  = {2022},

date = {2022-01-01},

journal = {The American Journal of Cardiology},

volume = {172},

pages = {18–25},

abstract = {The association of left ventricular ejection fraction (LVEF) with procedural and long-term outcomes after state-of-the-art percutaneous coronary intervention (PCI) of bifurcation lesions remains unsettled. A total of 5,333 patients who underwent contemporary coronary bifurcation PCI were included in the intercontinental retrospective combined insights from the unified RAIN (veRy thin stents for patients with left mAIn or bifurcatioN in real life) and COBIS (COronary BIfurcation Stenting) III bifurcation registries. Of 5,003 patients (93.8%) with known baseline LVEF, 244 (4.9%) had LVEF <40% (bifurcation with reduced ejection fraction [BIFrEF] group), 430 (8.6%) had LVEF 40% to 49% (bifurcation with mildly reduced ejection fraction [BIFmEF] group) and 4,329 (86.5%) had ejection fraction (EF) ≥50% (bifurcation with preserved ejection fraction [BIFpEF] group). The primary end point was the Kaplan-Meier estimate of major adverse cardiac events (MACEs) (a composite of all-cause death, myocardial infarction, and target vessel revascularization). Patients with BIFrEF had a more complex clinical profile and coronary anatomy. No difference in procedural (30 days) MACE was observed across EF categories, also after adjustment for in-study outcome predictors (BIFrEF vs BIFmEF: adjusted hazard ratio [adj-HR] 1.39, 95% confidence interval [CI] 0.37 to 5.21},

keywords = {ai, cardio},

pubstate = {published},

tppubtype = {article}

}

@article{21:ajc:bifurcat,

title = {Benefit of Extended Dual Antiplatelet Therapy Duration in Acute Coronary Syndrome Patients Treated with Drug Eluting Stents for Coronary Bifurcation Lesions (from the BIFURCAT Registry)},

author = {Ovidio De Filippo and Jeehoon Kang and Francesco Bruno and Jung-Kyu Han and Andrea Saglietto and Han-Mo Yang and Giuseppe Patti and Kyung-Woo Park and Radoslaw Parma and Hyo-Soo Kim and Leonardo De Luca and Hyeon-Cheol Gwon and Mario Iannaccone and Woo Jung Chun and Grzegorz Smolka and Seung-Ho Hur and Enrico Cerrato and Seung Hwan Han and Carlo Mario and Young Bin Song and Javier Escaned and Ki Hong Choi and Gerard Helft and Joon-Hyung Doh and Alessandra Truffa Giachet and Soon-Jun Hong and Saverio Muscoli and Chang-Wook Nam and Guglielmo Gallone and Davide Capodanno and Daniela Trabattoni and Yoichi Imori and Veronica Dusi and Bernardo Cortese and Antonio Montefusco and Federico Conrotto and Iacopo Colonnelli and Imad Sheiban and Gaetano Maria Ferrari and Bon-Kwon Koo and Fabrizio D'Ascenzo},

url = {https://www.sciencedirect.com/science/article/pii/S0002914921006354},

doi = {10.1016/j.amjcard.2021.07.005},

issn = {0002-9149},

year  = {2021},

date = {2021-01-01},

journal = {The American Journal of Cardiology},

abstract = {Optimal dual antiplatelet therapy (DAPT) duration for patients undergoing percutaneous coronary intervention (PCI) for coronary bifurcations is an unmet issue. The BIFURCAT registry was obtained by merging two registries on coronary bifurcations. Three groups were compared in a two-by-two fashion: short-term DAPT (≤ 6 months), intermediate-term DAPT (6-12 months) and extended DAPT (>12 months). Major adverse cardiac events (MACE) (a composite of all-cause death, myocardial infarction (MI), target-lesion revascularization and stent thrombosis) were the primary endpoint. Single components of MACE were the secondary endpoints. Events were appraised according to the clinical presentation: chronic coronary syndrome (CCS) versus acute coronary syndrome (ACS). 5537 patients (3231 ACS, 2306 CCS) were included. After a median follow-up of 2.1 years (IQR 0.9-2.2), extended DAPT was associated with a lower incidence of MACE compared with intermediate-term DAPT (2.8% versus 3.4%, adjusted HR 0.23 [0.1-0.54], p <0.001), driven by a reduction of all-cause death in the ACS cohort. In the CCS cohort, an extended DAPT strategy was not associated with a reduced risk of MACE. In conclusion, among real-world patients receiving PCI for coronary bifurcation, an extended DAPT strategy was associated with a reduction of MACE in ACS but not in CCS patients.},

keywords = {ai, cardio},

pubstate = {published},

tppubtype = {article}

}

@article{21:lancet,

title = {Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets},

author = {Fabrizio D'Ascenzo and Ovidio De Filippo and Guglielmo Gallone and Gianluca Mittone and Marco Agostino Deriu and Mario Iannaccone and Albert Ariza-Solé and Christoph Liebetrau and Sergio Manzano-Fernández and Giorgio Quadri and Tim Kinnaird and Gianluca Campo and Jose Paulo Simao Henriques and James M Hughes and Alberto Dominguez-Rodriguez and Marco Aldinucci and Umberto Morbiducci and Giuseppe Patti and Sergio Raposeiras-Roubin and Emad Abu-Assi and Gaetano Maria De Ferrari and Francesco Piroli and Andrea Saglietto and Federico Conrotto and Pierluigi Omedé and Antonio Montefusco and Mauro Pennone and Francesco Bruno and Pier Paolo Bocchino and Giacomo Boccuzzi and Enrico Cerrato and Ferdinando Varbella and Michela Sperti and Stephen B. Wilton and Lazar Velicki and Ioanna Xanthopoulou and Angel Cequier and Andres Iniguez-Romo and Isabel Munoz Pousa and Maria Cespon Fernandez and Berenice Caneiro Queija and Rafael Cobas-Paz and Angel Lopez-Cuenca and Alberto Garay and Pedro Flores Blanco and Andrea Rognoni and Giuseppe Biondi Zoccai and Simone Biscaglia and Ivan Nunez-Gil and Toshiharu Fujii and Alessandro Durante and Xiantao Song and Tetsuma Kawaji and Dimitrios Alexopoulos and Zenon Huczek and Jose Ramon Gonzalez Juanatey and Shao-Ping Nie and Masa-aki Kawashiri and Iacopo Colonnelli and Barbara Cantalupo and Roberto Esposito and Sergio Leonardi and Walter Grosso Marra and Alaide Chieffo and Umberto Michelucci and Dario Piga and Marta Malavolta and Sebastiano Gili and Marco Mennuni and Claudio Montalto and Luigi Oltrona Visconti and Yasir Arfat},

url = {https://www.researchgate.net/profile/James_Hughes3/publication/348501148_Machine_learning-based_prediction_of_adverse_events_following_an_acute_coronary_syndrome_PRAISE_a_modelling_study_of_pooled_datasets/links/6002a81ba6fdccdcb858b6c2/Machine-learning-based-prediction-of-adverse-events-following-an-acute-coronary-syndrome-PRAISE-a-modelling-study-of-pooled-datasets.pdf},

doi = {10.1016/S0140-6736(20)32519-8},

issn = {0140-6736},

year  = {2021},

date = {2021-01-01},

journal = {The Lancet},

volume = {397},

number = {10270},

pages = {199–207},

abstract = {Background The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratification model to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after ACS. Methods Different machine learning models for the prediction of 1-year post-discharge all-cause death, myocardial infarction, and major bleeding (defined as Bleeding Academic Research Consortium type 3 or 5) were trained on a cohort of 19826 adult patients with ACS (split into a training cohort [80%] and internal validation cohort [20%]) from the BleeMACS and RENAMI registries, which included patients across several continents. 25 clinical features routinely assessed at discharge were used to inform the models. The best-performing model for each study outcome (the PRAISE score) was tested in an external validation cohort of 3444 patients with ACS pooled from a randomised controlled trial and three prospective registries. Model performance was assessed according to a range of learning metrics including area under the receiver operating characteristic curve (AUC). Findings The PRAISE score showed an AUC of 0.82 (95% CI 0.78-0.85) in the internal validation cohort and 0.92 (0.90-0.93) in the external validation cohort for 1-year all-cause death; an AUC of 0.74 (0.70-0.78) in the internal validation cohort and 0.81 (0.76-0.85) in the external validation cohort for 1-year myocardial infarction; and an AUC of 0.70 (0.66-0.75) in the internal validation cohort and 0.86 (0.82-0.89) in the external validation cohort for 1-year major bleeding. Interpretation A machine learning-based approach for the identification of predictors of events after an ACS is feasible and effective. The PRAISE score showed accurate discriminative capabilities for the prediction of all-cause death, myocardial infarction, and major bleeding, and might be useful to guide clinical decision making.},

keywords = {ai, cardio, deephealth, hpc4ai},

pubstate = {published},

tppubtype = {article}

}